Proposing the online community self-disclosure model: the case of working professionals in France and the U.K. who use online communities View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-04

AUTHORS

Clay Posey, Paul Benjamin Lowry, Tom L Roberts, T Selwyn Ellis

ABSTRACT

The global use of online communities has exploded to involve hundreds of millions of users. Despite the tremendous social impact and business opportunities afforded by these communities, little information systems (IS) research has addressed them – especially in a cross-cultural context. Our research proposes an online community self-disclosure model, tested in a cross-cultural setting using data provided by French and British working professionals. Our model is based on social exchange theory (SET) and social penetration theory (SPT), as well as on cross-cultural theory related to individualism-collectivism. SET explains that individuals engage in relationships when the perceived costs associated with the relationship are less than the expected benefits. SPT extends SET to explain that individuals participate in self-disclosure to foster relationships – reciprocation is the primary benefit of self-disclosure, whereas risk is the foundational cost of self-disclosure. Our study established several important findings: positive social influence to use an online community increases online community self-disclosure; reciprocity increases self-disclosure; online community trust increases self-disclosure; and privacy risk beliefs decrease self-disclosure. Meanwhile, a tendency toward collectivism increases self-disclosure. We further found that French participants had higher scores on horizontal individualism than British participants. Several other findings and their implications for practice are also discussed. More... »

PAGES

181-195

Identifiers

URI

http://scigraph.springernature.com/pub.10.1057/ejis.2010.15

DOI

http://dx.doi.org/10.1057/ejis.2010.15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052951770


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